Modeling and simulation system for optimizing prosthetic heart valve treament

ABSTRACT

A computer-implemented method for simulating blood flow through one or more coronary blood vessels may first involve receiving patient-specific data, including imaging data related to one or more coronary blood vessels, and at least one clinically measured flow parameter. Next, the method may involve generating a digital model of the one or more coronary blood vessels, based at least partially on the imaging data, discretizing the model, applying boundary conditions to a portion of the digital model that contains the one or more coronary blood vessels, and initializing and solving mathematical equations of blood flow through the model to generate computerized flow parameters. Finally, the method may involve comparing the computerized flow parameters with the at least one clinically measured flow parameter.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 16/578,030, filed Sep. 20, 2019, which is continuation of U.S.patent application Ser. No. 14/850,648, filed Sep. 10, 2015, now U.S.Pat. No. 10,497,476, issued Dec. 3, 2019, which is a continuation ofU.S. patent application Ser. No. 14/264,544, filed Apr. 29, 2014, nowU.S. Pat. No. 9,135,381, issued Sep. 15, 2015, which claims priority toU.S. Provisional Patent Application No. 61/822,133, filed May 10, 2013.The full disclosures of all of the above-listed patent applications arehereby incorporated by reference herein.

FIELD

The present disclosure relates generally to the field of computer-aidedmodeling and simulation. More specifically, the disclosure relates tocomputer-based systems and methods for modeling cardiac anatomy andphysiology for simulation, therapeutic, treatment, and/or diagnosticpurposes.

BACKGROUND

Cardiovascular disease is the leading cause of death in the UnitedStates and claims the lives of more than 600,000 Americans each year.According to the World Health Organization, cardiovascular disease isthe leading cause of death worldwide and claims the lives ofapproximately 7 million people per year. Further, according to theAmerican Heart Association (AHA), more than five million Americans arediagnosed with heart valve disease, which is a form of cardiovasculardisease, each year, and diseases of the aortic and mitral valves are themost prevalent. Combined, aortic and mitral valve diseases affect morethan five percent of the U.S. population. Hence, it is clear thatcardiovascular disease, and heart valve disease in particular, is amajor health concern and impacts the lives of numerous people.

Aortic stenosis (AS), which is a form of aortic valve disease, is aubiquitous and potentially life-threatening disease that impactsapproximately 1.5 million people in the United States and is the thirdmost common cardiovascular disorder in the western world. Aorticstenosis is a general term that characterizes the abnormal operation ofthe heart valve that separates the left ventricle from the ascendingaorta, and AS may or may not be symptomatic. A stenosed aortic valve(AV) that does not open completely leads to abnormal blood flow throughthe valve and the aortic root. These abnormal flow patterns may lead toincreased vascular resistance and insufficient downstream perfusion. Inaddition, an AV that does not close properly may lead to aorticregurgitation (AR), in which reverse flow traverses the AV duringdiastole when the valve is supposed to be closed completely.

Mitral regurgitation (MR), which is a form of mitral valve disease, isalso a widespread and potentially life-threatening disease. In theUnited States, the occurrence of MR increases with age. In a studyconducted in 2000, at least moderate MR was observed in 0.5% ofparticipants aged 18 to 44 years and in 9.3% of participants aged 75years or greater. In Europe, MR is the second most frequent valvulardisease requiring surgery. Similar to aortic regurgitation, mitralregurgitation is a general term that characterizes the abnormaloperation of the mitral valve, which is the valve that separates theleft atrium from the left ventricle. When the mitral valve does notclose properly, blood may leak from the ventricle into the atrium duringcontraction of the left ventricle and thereby decrease the pumpingefficiency of the heart. In contrast to dysfunctional aortic valves,dysfunctional mitral valves may be repaired and may not requirereplacement.

The prognosis of patients with severe, untreated valvular heart diseaseis poor. In the case of AS, for example, clinical studies of untreatedpatients have demonstrated that survival rates are as low as 50% at twoyears and 20% at five years after the onset of symptoms. Further, acutemitral regurgitation is poorly tolerated and carries a poor prognosis inthe absence of treatment. Therefore, it is evident that patients withsymptomatic, severely diseased heart valves should seek treatment.

Accurate clinical diagnosis is instrumental in determining the severityand nature of heart valve disease. The American College of Cardiology(ACC) and the AHA have published medical guidelines that helpcharacterize the clinical indications for valvular heart disease and thecorresponding clinical treatments. In the context of AS, diagnosis isdependent on the quantitative values of various blood flow parameters aswell as a visual inspection of the valve and its operation. The outcomeof a patient examination may be a diagnosis of mild, moderate, severe orcritical AS. Per society guidelines, only patients with symptomatic,severe or critical AS may be candidates for aortic valve replacement(AVR), which usually involves open heart surgery. Similarly, the ACC andAHA have published guidelines to help diagnose and treat diseases of theother three heart valves, and these diagnostic methods are based onanalysis of medical images and characteristics of the blood flow.

Despite the apparent need for treatment, an increasing number ofpatients with symptomatic, severe AS are ineligible for open heartsurgery and surgical AVR. Ineligibility for open chest surgery may bedue to significant co-morbidities, such as high surgical risk, advancedage, history of heart disease or frailty. These patients have a poorprognosis and may benefit greatly from alternative therapies andtreatments that do not require open chest surgery.

For patients deemed inoperable or who do not wish to undergo an invasivesurgical operation, minimally invasive or transcatheter valveimplantation may be an option for improving valvular function,alleviating symptoms, and improving quality of life. Transcatheteraortic valve replacement (TAVR), for example, is a minimally-invasiveapproach to replace the malfunctioning native aortic valve with afunctional prosthetic valve. During a TAVR procedure, a prostheticaortic valve is typically inserted via a catheter that is introduced viaa femoral or transapical pathway. In contrast to surgical AVR, TAVR doesnot require a sternotomy (incision in the center of the chest thatseparates the chestbone to allow access to the heart), and a heart-lungmachine is not needed because the heart is not stopped. Further, becausethe TAVR procedure is less invasive than surgical AVR, patientsgenerally spend less time in the hospital, experience shorter recoverytimes, and may be less reluctant to undergo the procedure. Transcathetervalve implantation may also be an option to repair other heart valvessuch as the mitral or pulmonary valve. Alternatively, sutureless heartvalves provide a minimally invasive mechanism for heart valvereplacement.

Despite the apparent benefits of transcatheter valve replacement, thereare serious clinical risks associated with the procedure. In the case ofTAVR, for example, clinically significant post-procedural AR is afrequent problem and occurs in up to 50% of patients.

Further, results from clinical trials suggest a linear relationshipbetween the severity of post-procedural AR and 1- and 2-year mortality,and even mild AR may be associated with increased mortality. Therefore,to maximize the potential benefits of TAVR and minimize the long-termrisks to patient well-being, AR should be minimized as much as possible.Other risks of transcatheter valve replacement, which are applicable toall percutaneously deployed heart valves, include stroke, vascularcomplications, improper deployment, obstruction of secondary vessels(e.g., coronary ostium), and valve migration.

Minimizing the risks of negative complications following transcathetervalve implantation requires careful pre-surgical planning and executionof the procedure. Valvular regurgitation in the presence oftranscatheter aortic heart valves, for example, is often due to a largemean annulus size, valvular calcification, and/or improper sizing of thevalve. Specifically, paravalvular regurgitation (i.e., undesired,reverse flow—or leakage—that occurs between the perimeter of theprosthetic valve and the aortic annulus) is a frequent occurrence withaortic valves and is often caused by improper valve sizing. In contrastto surgical valve replacement, wherein the surgeon may visually inspectthe anatomic structure of the native valve and surrounding vasculaturebefore implanting the prosthetic valve, transcatheter approachescurrently rely on clinical imaging techniques (e.g., echocardiography,computed tomography, magnetic resonance imaging) for sizing,positioning, and deploying the prosthesis. These images may not provideaccurate anatomic information suitable for precise planning anddeployment of transcatheter valves, which may contribute to therelatively high incidence of complications (e.g., valvularregurgitation).

Two-dimensional images of inherently three-dimensional anatomy mayprovide inaccurate information for planning and executing transcatheterand minimally invasive procedures. In addition, imaging modalities withrelatively low spatial resolution (e.g., ultrasound) may be unable toresolve anatomic structures that are critical for pre-surgical planning.In the context of TAVR, for example, relatively low resolutiontwo-dimensional echocardiographic images are known to underestimate thesize of the aortic annulus; the size of the annulus is used to selectthe size of the prosthetic valve. This underestimation of vasculardimension may lead to deployment of a relatively small prosthetic valveand thereby contribute to a high incidence of paravalvular regurgitationbecause the prosthetic valve is too small to fill the native annulus. Incontrast, relatively high resolution two-dimensional computed tomography(CT) imaging is known to overestimate the size of the aortic annulus,and prosthetic valve sizing based on CT measurements often leads to alower incidence of regurgitation.

Hence, while proper sizing and pre-procedural planning of transcatheterand minimally invasive heart valve procedures is widely recognized as anessential component for maximizing clinical benefits, the means by whichthese heart valves are sized requires appreciable clinical judgment andis prone to error.

Therefore, it would be very desirable to have a system and method foraccurately assessing the anatomic size and morphology of heart valvesand the surrounding vasculature. Such a system and method would ideallyfacilitate proper selection, sizing, positioning, and pre-surgicalplanning of prosthetic heart valve procedures. Such systems should notexpose patients to excessive risks.

DESCRIPTION OF RELATED ART

There are many academic and industrial research groups that use computermodeling and simulation to analyze flow through heart valves.Historically, valvular hemodynamic analyses have focused on the aorticheart valve and have employed methods of computational fluid dynamics(CFD) to provide detailed insight into the blood flow surrounding theaortic valve. These insights have then been used to facilitate thedesign and construction of heart valves with optimal or near optimalhemodynamic properties that maximize functionality and durability whileminimizing the potentially fatal risks of valvular malfunction andadverse response.

In recent years, hemodynamic modeling of heart valves has included bothsurgically implanted and transcatheter prostheses, but the focus of moststudies remains the aortic valve. With the rapidly expanding clinicaldeployment of transcatheter aortic heart valves, modeling and simulationhas helped understand and characterize the unique hemodynamic challengesof transcatheter deployment in comparison to traditional surgicalimplantation of aortic valves. In particular, computer modeling has beenused to quantify valvular regurgitation, downstream flow effects in theaortic arch, leaflet stresses, vascular response, and othercharacteristics of valvular implantation that impact device efficacy,robustness, durability, and longevity.

To date, all computer modeling and simulation studies of heart valvesare focused on evaluating and improving prosthetic valve design andfunction.

BRIEF SUMMARY

In contrast to currently available systems and methods for computermodeling of heart valves, the embodiments described herein involvemodeling and simulation systems and methods that may be used tofacilitate the selection, sizing, deployment, and/or pre-surgicalplanning of prosthetic heart valves. The systems and methods may also beused to diagnose and assess diseased heart valves. Unlike currentlyavailable systems, the embodiments described herein are directed towardanatomic assessment for diagnostic and pre-surgical planning purposes(e.g., device selection, sizing, deployment), rather than device designand function. In various embodiments, the systems and methods may beapplied to any one or more heart valves.

The modeling and simulation system described herein uses computermodeling to facilitate sizing and deployment of transcatheter heartvalves (e.g., aortic valve, mitral valve). In addition to using anatomicand geometric data gathered through two- and/or three-dimensionalimaging studies, the modeling and simulation system also incorporatesphysiologic (e.g., hemodynamic) data into the construction of anaccurate anatomic model that serves as the basis for diagnosis andsurgical planning/execution. Hemodynamic data, which are currentlyexcluded from all valvular sizing methods, provide three-dimensionalinsight into local valvular morphology, which enables an accuratephysiologic assessment for prosthesis sizing and deployment. The sizingand deployment data obtained from the modeling and simulation systemprovide physicians with clinically relevant information that enablesinformed decision-making and thereby reduces the possibilities ofadverse clinical events (e.g., valvular regurgitation). In addition, thesystem also facilitates sensitivity and uncertainly analyses, therebyenabling the complete and accurate planning of heart valve implantation.

In one aspect, a computer-implemented method for simulating blood flowthrough a heart valve may first involve receiving patient-specific data,including imaging data related to the heart valve, an inflow tract ofthe heart valve and an outflow tract of the heart valve, and at leastone clinically measured flow parameter. Next, the method may involvegenerating a digital model of the heart valve and the inflow and outflowtracts, based at least partially on the imaging data, discretizing themodel, applying boundary conditions to a portion of the digital modelthat contains the heart valve and the inflow and outflow tracts, andinitializing and solving mathematical equations of blood flow throughthe model to generate computerized flow parameters. Finally, the methodmay involve comparing the computerized flow parameters with the at leastone clinically measured flow parameter. Optionally, the method mayfurther involve adjusting the digital model after the comparison step.In some embodiments, the method may also involve, after adjusting thedigital model, re-solving the mathematical equations to generate newcomputerized flow parameters. The method may further include comparingthe new computerized flow parameters with the clinically measured flowparameters.

In some embodiments, the patient-specific data may be derived from onlynon-interventional data collection method(s) and/or minimally invasivedata collection method(s). In some embodiments, generating the digitalmodel may involve generating the model based at least partially on theimaging data and at least partially on the clinically measured flowparameter(s). Optionally, some embodiments may further involveperforming a sensitivity analysis and/or an uncertainty analysis on thecomputerized flow parameters.

In various embodiments, the digital model may be used for diagnosing adisease state, assessing a disease state, determining a prognosis of adisease state, monitoring a disease state, planning a prosthetic heartvalve implantation and/or performing a prosthetic heart valveimplantation. The imaging data may be derived from any suitable imagingmodality, such as but not limited to echocardiography, ultrasound,magnetic resonance imaging, x-ray, optical tomography and/or computedtomography. The clinically measured flow parameter(s) may be measuredusing any suitable modality, such as but not limited to Dopplerechocardiography, catheterization and/or functional magnetic resonance.

In another aspect, a computer-implemented method for generating ananatomical model of a heart valve may include: receivingpatient-specific imaging data of the heart valve and inflow and outflowtracts of the heart valve; generating a digital anatomical model of theheart valve and the inflow and outflow tracts, based at least partiallyon the imaging data; modeling blood flow through the digital model togenerate a first set of computerized flow parameters; comparing thefirst set of computerized flow parameters with at least one clinicallymeasured flow parameter; adjusting the digital model, based on thecomparison of the first set of computerized flow parameters with theclinically measured flow parameters; modeling blood flow through theadjusted digital model to generate a second set of computerized flowparameters; and comparing the second set of computerized flow parameterswith the at least one clinically measured flow parameter.

In some embodiments, the method may further involve, before adjustingthe digital model, determining, based on the comparison of the first setof parameters with the clinically measured parameters, that the digitalmodel is unacceptable. For example, determining that the digitalanatomical model is unacceptable may involve determining that the firstset of computerized flow parameters differs from the at least oneclinically measured flow parameter by at least a predetermined thresholdamount. In some embodiments, generating the first set of computerizedflow parameters may involve: discretizing the digital model; applyingboundary conditions to a portion of the digital model that contains theheart valve and the inflow and outflow tracts; and initializing andsolving mathematical equations of blood flow through the digital model.

In some embodiments, the method may further involve, after the secondcomparing step: adjusting the adjusted digital anatomical model, basedon the comparison of the second set of computerized flow parameters withthe at least one clinically measured flow parameter, to generate a newadjusted digital anatomical model; modeling blood flow through the newadjusted digital model to generate a third set of computerized flowparameters; and comparing the third set of computerized flow parameterswith the at least one clinically measured flow parameter. Someembodiments may involve repeating the adjusting, modeling and comparingsteps until a desired level of agreement is reached between a mostrecently calculated set of computerized flow parameters and the at leastone clinically measured flow parameter.

In another aspect, a system for generating an anatomical model of aheart valve may include at least one computer system configured to:receive patient-specific imaging data of the heart valve and inflow andoutflow tracts of the heart valve; generate a first digital anatomicalmodel of the heart valve and the inflow and outflow tracts, based atleast partially on the imaging data; model blood flow through the firstmodel to generate a first set of computerized flow parameters; comparethe first set of computerized flow parameters with clinically measuredflow parameters; adjust the first digital anatomical model, based on thecomparison of the first set of computerized flow parameters with theclinically measured flow parameters, to generate a second digitalanatomical model; model blood flow through the second model to generatea second set of computerized flow parameters; and compare the second setof computerized flow parameters with the clinically measured flowparameters.

Optionally, the computer system may be further configured to determine,before the adjusting step and based on the comparison of the first setof parameters with the clinically measured parameters, that the firstdigital anatomical model is unacceptable. For example, to determine thatthe first digital anatomical model is unacceptable, the at least onecomputer system may be configured to determine that the first set offlow parameters differs from the clinically measured flow parameters byat least a predetermined threshold amount. In some embodiments, thecomputer system may be further configured to repeat the determining,adjusting, modeling and comparing steps until a desired level ofagreement is reached between a most recently calculated set ofcomputerized flow parameters and the at least one clinically measuredflow parameter.

In some embodiments, to generate the first set of computerized flowparameters, the computer system may be configured to: discretize thefirst model; apply boundary conditions to a portion of the first modelthat contains the heart valve and the inflow and outflow tracts; andinitialize and solve mathematical equations of blood flow through thefirst model.

These and other aspects and embodiments will be described in furtherdetail below, in reference to the attached drawing figures.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a flow diagram, outlining a method for modeling andsimulation, according to one embodiment;

FIG. 2 is a perspective view of a simplified geometric model, based onpatient-specific anatomic parameters, of the aortic valve and thesurrounding cardiac inflow and outflow vessels, according to oneembodiment;

FIG. 3 is a perspective view of a simplified geometric model with thecomputational surface mesh, based on patient-specific anatomicparameters, of the aortic valve and the surrounding cardiac inflow andoutflow vessels, according to one embodiment; and

FIGS. 4A-4D are perspective views of representative polyhedra used todiscretize the interior volume of the geometric model, according to oneembodiment.

DETAILED DESCRIPTION

This disclosure describes computer modeling and simulation systems andmethods that qualitatively and quantitatively characterize anatomicgeometry of a heart valve and/or the corresponding inflow/outflow tractsof the heart. The various embodiments described herein may be applied toany single heart valve, a combination of multiple heart valves, and/orcombinations of one or more heart valves and one or more coronary bloodvessels. Although occasional references may be made to one specificheart valve, these specific references should not be interpreted aslimiting the scope of this disclosure. For example, the aortic heartvalve is occasionally used throughout this disclosure as a specificexample of a prototypical heart valve. Illustration of the systems andmethods via the example of the aortic heart valve, however, is notintended to limit the scope of the computer modeling and simulationsystems and methods disclosed herein.

Referring to FIG. 1, one embodiment of a method for implementing amodeling and simulation system is illustrated. A first step of themethod may involve importing or receiving patient-specific geometric,anatomic, physiologic, and/or hemodynamic data into the computer system100. Typically, this patient-specific data includes at least someimaging data and at least one clinically measured flow parameter. Invarious embodiments, the imaging data and the clinically measured flowparameter(s) may be received by the system at the same time or atdifferent times during the process. The system may receive data from anynumber and/or any type of patient-specific data collection source ormodality. In some embodiments, all the data received may be datagenerated from non-invasive and/or minimally invasive modalities.Examples of imaging modalities from which data may be received include,but are not limited to, echocardiography, ultrasound, magnetic resonanceimaging (MM), x-ray, optical tomography such as optical coherencetomography (OCT) and computed tomography (CT). Examples of modalitieswhich may be used for generating the received clinically measured flowparameter(s) include, but are not limited to, Doppler echocardiography,catheterization procedures, functional magnetic resonance, routineclinical tests (e.g., blood pressure, heart rate) and/or tests otherwiseprescribed by physicians to diagnose abnormal function of the cardiacchambers or one or more heart valves.

The second step (FIG. 1) may involve constructing a (possiblyparameterized) geometric model, using the imported/received data 200. Atypical geometric model 10, as illustrated in FIG. 2, may be amulti-dimensional digital representation of the relevant patientanatomy, which may include at least one heart valve 12, at least aportion of an inflow vessel 14 (or “inflow tract”), and at least aportion of an outflow vessel 16 (or “outflow tract”) of thecorresponding valve 12. The model may also include one or moreventricles and/or atria of the heart or a portion thereof. The geometricmodel is created from patient-specific anatomical, geometric,physiologic, and/or hemodynamic data. In some embodiments, the model maybe created 200 using exclusively imaging data. Alternatively, the modelmay be created 200 using imaging data and at least one clinicallymeasured flow parameter. Imaging data may be obtained from any suitablediagnostic imaging exam(s), such as those listed above. Clinicallymeasured flow parameters may be obtained from any suitable test(s), suchas those listed above.

The model may also contain at least one inflow boundary and at least oneoutflow boundary through which blood flows in and out of themulti-dimensional model, respectively. These inflow and outflowboundaries may denote finite truncation of the digital model and may notbe physically present in a patient. The digital geometric model may becreated using methods of applied mathematics and image analysis, such asbut not limited to image segmentation, machine learning, computer aideddesign, parametric curve fitting, and polynomial approximation. Invarious embodiments, a hybrid approach, which combines a collection ofgeometric modeling techniques, may also be used. The final,multi-dimensional model provides a digital surrogate that captures therelevant physical features of the anatomic topology under considerationand may contain one or more morphological simplifications that exploitthe underlying geometric features of the patient-specific valvular andvascular system under consideration. Such simplifications may, forexample, involve mathematical transformations (e.g., geometricsmoothing) or the exclusion of anatomic structures (e.g., chordaetendineae of the mitral valve).

Referring again to FIG. 1, following the construction of the digitalmodel 200, the modeling and simulation system may discretize the surfaceand volume of the model into a finite number of partitions 300. Theseindividual and non-overlapping partitions, termed elements, facilitatethe application and solution of the physical laws of motion that governblood flow through the geometric model. The set of surface and volumeelements used to discretize the model, collectively referred to as thecomputational mesh, transforms the continuous geometric model into a setof mesh points and edges, where each element point in the computationalmesh has discrete x, y, and z spatial coordinates; each element edge isbounded by two mesh points and has a finite length.

An illustration of a representative mesh 21 that discretizes the surfaceof a geometric model 20 is shown in FIG. 3. FIG. 3 is a perspective viewof a geometric model 20, including an aortic valve 22, inflow tract 24and outflow tract 26. This illustration of the model 20 is used to showthe mesh 21.

Referring to FIGS. 4A-4D, the shape of the surface elements created bythe modeling and simulation system may take the form of any closedpolygon, but the surface mesh typically contains a collection oftriangles, convex quadrilaterals or a combination thereof. Volumeelements are created by the modeling and simulation system and are usedto fill the interior of the model completely. Each volume element maytake the form of any closed polyhedron, but the volume mesh (i.e., theset of volume elements) typically contains a collection of tetrahedra,hexahedra, wedges or a combination thereof (FIGS. 4A-4D). The surfaceand volume mesh densities, which determine the spatial resolution of thediscrete model, may vary in space and time, as illustrated in FIG. 3.The local densities of the surface and volume meshes may depend on thecomplexity of the local topology of the underlying geometric model: morecomplex local topology may require higher spatial resolution, andtherefore a higher mesh density, to resolve than local regions of lesscomplex topology (e.g., see FIG. 3 (right) near the aortic valve 22).

The modeling and simulation system may use CFD to simulate blood flowthrough the discretized geometric model. Blood may be represented as aNewtonian or non-Newtonian fluid, and blood flow may be representedphysically by the conservation of mass, momentum, and energy (or acombination thereof) and mathematically by the fluid flow equations(e.g., continuity, Navier-Stokes equations) with appropriate initial andboundary conditions; the boundary conditions may be constant or afunction of time and/or space, and the boundary conditions may bedifferent at different inflow/outflow surfaces. Initial and boundaryconditions may be determined from empirical or heuristic relationships,clinical data, mathematical formulas or a combination thereof, and themodel boundaries may be rigid or compliant or a combination thereof. Themathematical equations and corresponding initial and boundary conditionsmay be solved using conventional mathematical techniques, which includeanalytical or special functions, numerical methods (e.g., finitedifferences, finite volumes, finite elements, spectral methods), methodsof machine learning or a hybrid approach that combines various aspectsof the methods listed.

As a next step in the modeling and simulation method, and referringagain to FIG. 1, the one or more boundary conditions may be applied to adiscrete patient model 400. The boundary flow conditions may be obtainedfrom patient-specific clinical measures (e.g., Doppler echocardiography,MRI), in which case they may be applied to the model in a manner that isconsistent with clinical observations and measurements. In addition,inflow and outflow boundary conditions may be applied to compensate forunderlying psychological or medical conditions such as pain, anxiety,fear, anemia, hyperthyroidism, left ventricular systolic dysfunction,left ventricular hypertrophy, hypertension or arterial-venous fistula,which may produce clinically misleading results, upon which medicaldiagnoses and treatments may be based.

Referencing FIG. 1 and following the initialization of the blood flowequations, the equations may be solved, and hemodynamic quantities maybe computed, by the modeling and simulation system 500. The blood flowequations may be solved in a steady-state or time-dependent fashion; ahybrid approach that combines steady-state and time-dependent methodsmay also be used. Next, computed hemodynamic quantities may be comparedwith corresponding quantities obtained from clinical measurements,tests, and/or examinations (e.g., Doppler echocardiography,catheterization procedures, functional magnetic resonance or phasecontrast MRI) 600. If the computed and clinically measured hemodynamicquantities are in satisfactory agreement 600 a, then the results of themodeling and simulation system may be analyzed and information or areport may be delivered to a physician(s) or another medicalprofessional 700. If the computed and clinically measured hemodynamicquantities are not in satisfactory agreement 600 b, the patient-specificmodel may be modified in a manner thought to increase agreement betweencomputed and clinical hemodynamic quantities, and a new computation maybe performed with the modified model. Steps 300-600 may then be repeateduntil satisfactory agreement between computed and clinical data isobtained, and information or a report may be delivered to a physician(s)or another medical professional 700.

As an illustrative example of the embodiments described in 600, 600 a,and 600 b of FIG. 1, the clinically measured (via Dopplerechocardiography, for example) peak velocity distal to the AV may becompared with the corresponding numerical value computed (via CFD) bythe modeling and simulation system. If the computed and clinicalvelocities are agreeable to within a specified accuracy tolerance(s),then the geometric and hemodynamic models may be deemed accurate 600 a,and information or a report that details the geometric and/orhemodynamic results may be delivered to a physician(s) or anothermedical professional 700. If, however, the computationally computed peakvelocity and the clinically measured peak velocity fail to meet thespecified accuracy tolerance(s) 600 b, then the geometric and/orhemodynamic model may be adjusted and the flow may be recomputed viaCFD. The new peak velocity distal to the AV that is computed with thenew geometric and/or hemodynamic model may then be compared with thecorresponding clinical velocity per 600 of FIG. 1. This iterativeprocess of modifying the geometric and/or hemodynamic model, recomputingthe flow, and comparing the computed and clinical velocities may berepeated until the computationally computed flow quantities and theclinically measured flow quantities are in satisfactory agreement.

After satisfactory agreement is achieved, the iterative process may beterminated, and information or a report that details the geometricand/or hemodynamic results may be delivered to a physician(s) or medicalprofessional, per 700. In this illustrative example, the intent ofadjusting the geometric and/or hemodynamic model is to maximizeagreement between the computationally computed and clinically measuredpeak velocity distal to the AV, thereby ensuring the construction of anaccurate geometric and hemodynamic model. In some embodiments,characterizing and understanding the similarities and differencesbetween the clinically measured and/or derived results and thecorresponding modeling and simulation system results may be used toadjust modeling parameters and maximize agreement between the clinicallymeasured and/or derived results and those results numerically computedby the modeling and simulation system. These similarities anddifferences, as well as additional geometric and/or hemodynamicinformation provided by the modeling and simulation system, may also beused to guide clinical diagnoses and decision-making.

Output of each CFD analysis may include qualitative and/or quantitativegeometric and hemodynamic information that may be computed directly fromthe CFD analysis and/or through one or more mechanisms ofpost-processing. These numerical results may be analyzed to revealpatient-specific anatomic, geometric, physiologic, and/or hemodynamicinformation that aid in the construction of an accurate and inclusivemodel at a single time or at a multitude of points in time. Thesequalitative and quantitative data may also be used to guide clinicaldecision-making and/or predictive information about disease state,progression or risk stratification.

Output data from the modeling and simulation system may be delivered tophysicians or other medical professionals, who may use the data forclinical decision-making 700. Delivery of patient-specific informationto medical professionals may occur via verbal discussions, writtencorrespondence, electronic media or a combination thereof. These datamay then be used by an individual physician or by a team of physiciansto develop a complete, comprehensive, and accurate understanding ofpatient cardiac health and determine whether or not medical treatment iswarranted. If medical treatment is warranted, then results from themodeling and simulation system may be used to guide clinicaldecision-making. Specific ways in which output from the modeling andsimulation system may be incorporated into the clinical management ofcardiac patients include, but are not limited to: (1) analysis of heartvalve operation, including, for example, diagnosing the severity,functional significance, mechanism, and clinical response to abnormalheart valve operation; (2) pre-surgical planning of heart valveprocedures, including, for example, patient-specific selection, sizing,deployment mechanisms, and positioning of prosthetic heart valves forsurgical, minimally invasive, transcatheter or valve-in-valvetreatments; (3) post-surgical assessment of heart valve procedures,including, for example, regurgitation, gradients, velocities, pressures,placements or efficacy; and (4) patient monitoring and/or follow-up.This list of potential uses for the systems and methods described hereinis for example purposed only, and the list is not intended to beexhaustive.

The modeling and simulation system provides a virtual framework forconducting patient-specific sensitivity analyses. Such analyses mayassess the relative impacts of anatomic and/or physiologic changes tothe underlying anatomy and/or hemodynamic state of a patient. Thesestate changes may then be assessed for functional and clinicalsignificance, thereby estimating patient response to therapy, diseaseprogression, and/or patient-specific risk stratification. Sensitivityanalyses may be performed, for example, by coupling the modeling andsimulation system with Monte Carlo and/or adjoint-based numericalmethods that interact closely with the modeling and simulation systemdescribed above (FIG. 1). These numerical methods may bederivative-based or derivative-free and may enable numerous anatomic,geometric, physiologic, and/or hemodynamic scenarios to run in a virtualenvironment without exposing patients to any medical risks. Results fromthe plethora of simulations conducted during a sensitivity analysis maybe aggregated and presented to a medical professional to aid withclinical decision-making. Results from sensitivity analyses may also beused in conjunction with uncertainty analyses to assess global and/orlocal uncertainties of anatomic, geometric, physiologic, and/orhemodynamic results produced by the modeling and simulation system.

Uncertainty analysis may also be used to assess the clinical impact orsignificance of variabilityor unknown parameters associated with device(s) that may be deployedduring treatment (e.g., manufacturing tolerances).

The modeling and simulation system may enable planning of heart valvereplacement therapy and the selection of optimal valve deployment. Inparticular, executing the modeling and simulation system describedherein may provide an accurate assessment of anatomic, geometric,physiologic, and/or hemodynamic considerations for valvular deploymentand function, e.g., valve type, size, mechanism, angle and/or the like.Hence, the modeling and simulation systems and methods may provide acomplete framework that facilitates the accurate and complete anatomicand physiologic assessment of heart valves and their correspondinginflow/outflow tracts. This information may be used by medicalprofessionals to guide clinical decisions regarding patient treatment ofheart valve disease as to maximize the benefits to each patient.

Although the foregoing description is intended to be complete, any of anumber of acceptable additions, subtractions or alterations to thedescribed systems and methods may be made, without departing from thescope of the invention. For example, various method steps may beeliminated or performed in different order. Therefore, this descriptionis provided for exemplary purposes, and should not be interpreted aslimiting the scope of the invention.

1. A computer-implemented method including a processor for simulatingblood flow through a one or more coronary blood vessels, the methodcomprising: receiving patient-specific imaging data by said processorrelated to the one or more coronary blood vessels; receiving at leastone patient-specific measured flow parameter by said processor relatedto blood flow through the one or more coronary blood vessels; generatingby said processor a geometric model of the one or more coronary bloodvessels, based at least partially on the imaging data, the geometricmodel having modeling parameters; applying boundary conditions by saidprocessor, corresponding to desired flow, to a portion of the geometricmodel that contains the one or more coronary blood vessels, whereinapplying the boundary conditions comprises selecting boundary conditionsbased at least partially on patient-specific measurements; solvingmathematical equations of blood flow through the geometric model by saidprocessor to generate a first set of computerized flow parameters bysimulating blood flow through the model while the model characterizesphysical features of an anatomic topology of the one or more coronaryblood vessels; comparing by said processor the first set of computerizedflow parameters with the at least one measured flow parameter.
 2. Amethod as in claim 1, wherein selecting the boundary conditionscomprises selecting inflow and outflow boundary conditions thatcompensate for at least one of underlying psychological condition ormedical condition.
 3. A method as in claim 1, wherein receiving thepatient-specific imaging data comprises receiving at least one ofnon-interventionally generated data or minimally invasively generateddata.
 4. A method as in claim 1, wherein generating the geometric modelcomprises generating the geometric model based at least partially on theimaging data and at least partially on the at least one measured flowparameter.
 5. A method as in claim 1, further comprising performing atleast one of a sensitivity analysis or an uncertainty analysis on thefirst and second sets of computerized flow parameters.
 6. A method as inclaim 1, further comprising using the geometric model for at least oneof diagnosing a disease state, assessing a disease state, determining aprognosis of a disease state, monitoring a disease state, planningpatient treatment or performing patient treatment.
 7. A method as inclaim 1, wherein receiving the patient-specific imaging data comprisesreceiving the imaging data from an imaging modality selected from thegroup consisting of echocardiography, ultrasound, magnetic resonanceimaging, x-ray, optical tomography and computed tomography.
 8. A methodas in claim 1, wherein receiving the at least one measured flowparameter comprises receiving a parameter selected from the groupconsisting of Doppler echocardiograph, catheterization and a functionalmagnetic resonance image.
 9. A computer-implemented method including aprocessor for generating a geometric model of one or more coronary bloodvessels, the method comprising: receiving patient-specific imaging databy said processor of the one or more coronary blood vessels; generatingby said processor a geometric model of the one or more coronary bloodvessels, based at least partially on the imaging data, the geometricmodel having modeling parameters and model boundaries representingphysical features of an anatomic topology of the one or more coronaryblood vessels; modeling blood flow through the geometric model togenerate a first set of computerized flow parameters by said processor,wherein generating the first set of computerized flow parameterscomprises applying boundary conditions, corresponding to desired flow,to a portion of the geometric model that contains the one or morecoronary blood vessels, and wherein applying the boundary conditionscomprises selecting boundary conditions based at least partially onpatient-specific measures; comparing by said processor the first set ofcomputerized flow parameters with at least one measured flow parameter.10. A method as in claim 9, wherein generating the first set ofcomputerized flow parameters further comprises: discretizing thegeometric model; and solving mathematical equations of blood flowthrough the geometric model.
 11. A method as in claim 9, whereinreceiving the patient-specific imaging data comprises receiving at leastone of non-interventionally generated data or minimally invasivelygenerated data.
 12. A method as in claim 9, wherein receiving thepatient-specific imaging data comprises receiving the imaging data froman imaging modality selected from the group consisting ofechocardiography, ultrasound, magnetic resonance imaging, x-ray, opticaltomography and computed tomography.
 13. A method as in claim 9, whereinreceiving the at least one measured flow parameter comprises receiving aparameter selected from the group consisting of a Dopplerechocardiograph, a catheterization and a functional magnetic resonanceimage.
 14. A method as in claim 9, further comprising performing atleast one of a sensitivity analysis or an uncertainty analysis on thefirst set of computerized flow parameters.
 15. A method as in claim 9,further comprising using the adjusted geometric model for at least oneof diagnosing a disease state, assessing a disease state, determining aprognosis of a disease state, monitoring a disease state, planningpatient treatment or performing patient treatment.
 16. A system forgenerating a geometric model including a processor of one or morecoronary blood vessels, the system comprising at least one computersystem configured to: receive patient-specific imaging data by saidprocessor of the one or more coronary blood vessels; receive at leastone patient-specific measured flow parameter by said processor relatedto blood flow through the one or more coronary blood vessels; generateby said processor a first geometric model of the one or more coronaryblood vessels, based at least partially on the imaging data, thegeometric model having modeling parameters and model boundariesrepresenting physical features of an anatomic topology of the one ormore coronary blood vessels; model blood flow through the firstgeometric model by said processor to generate a first set ofcomputerized flow parameters, wherein generating the first set ofcomputerized flow parameters comprises applying boundary conditions,corresponding to desired flow, to a portion of the geometric model thatcontains the one or more coronary blood vessels, and wherein applyingthe boundary conditions comprises selecting boundary conditions based atleast partially on patient-specific measurements; compare by saidprocessor the first set of computerized flow parameters with measuredflow parameters.